A critical issue in modern cancer research is how diversity, both genetic and non-genetic, influences tumor progression and response to therapy. The Center for Cancer Systems Biology (CCSB) at MSKCC assembles a consortium of investigators who integrate computational and experimental strategies to investigate diversity in cancer at the level of individual cells, tumor microenvironment, and patients. The research program for this CCSB is organized into four inter-related subprojects. (I)We study the variability of cellular responses to growth factors and drugs during tumorigenesis, using competition for the growth factor IL-6 in melanoma and breast cancer cells as a model system. We use computational modeling of the dynamics of the IL-6 pathway in order to optimize new chemotherapeutic protocols that rely on cellular diversity to maximize tumor cytotoxicity. (II) We use expression profiling of protease networks in both tumor and stromal cells and genome-wide profiling of subpopulations of tumor-associated macrophages to learn predictive statistical models of tumor-stromal cell interactions. We also use agent-based computational models to simulate cancer-cell macrophage interactions. Computational predictions will be validated with in vitro and in vivo experiments. (Ill)We focus on predictive network models of differences in signaling information flow in distinct tumor subtypes. Using the results of systematic drug perturbation experiments, we will design Hopfield network models based on non-linear differential equations to decipher the differences in signaling networks between primary glioblastoma subtypes and to investigate the changes in network dynamics during the evolution of drug resistance. (IV) We study the endogenous diversity of B cell signaling pathways in Chronic Lymphocytic Leukemia (CLL) patients. We will generate biochemical models of signaling pathways to identify key signaling regulators whose variation in expression predicts functional heterogeneity, validate the predictions with single-cell profiling, and define a new set of functional markers to better characterize disease progression. By elucidating the consequences of diversity in cancer, this research will ultimately guide the development of new cancer therapies.